In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical...

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Bibliographic Details
Main Authors: Vinicius Pegorini, Leandro Zen Karam, Christiano Santos Rocha Pitta, Rafael Cardoso, Jean Carlos Cardozo da Silva, Hypolito José Kalinowski, Richardson Ribeiro, Fábio Luiz Bertotti, Tangriani Simioni Assmann
Format: Article
Language:English
Published: MDPI AG 2015-11-01
Series:Sensors
Subjects:
pattern classification
machine learning
ingestive behavior
biomechanical forces
fiber Bragg grating sensor (FBG)
Online Access:http://www.mdpi.com/1424-8220/15/11/28456

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